Overview

Dataset statistics

Number of variables15
Number of observations314
Missing cells483
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.2 KiB
Average record size in memory128.0 B

Variable types

Numeric13
Categorical1
DateTime1

Warnings

YEAR is highly correlated with formatted_dateHigh correlation
PM_RETIRO is highly correlated with PM_CIUDADLINEAL and 1 other fieldsHigh correlation
PM_CIUDADLINEAL is highly correlated with PM_RETIRO and 1 other fieldsHigh correlation
PM_CENTRO is highly correlated with PM_RETIRO and 1 other fieldsHigh correlation
DEW_POINT is highly correlated with TEMPERATUREHigh correlation
PREASSURE is highly correlated with TEMPERATUREHigh correlation
TEMPERATURE is highly correlated with DEW_POINT and 1 other fieldsHigh correlation
formatted_date is highly correlated with YEARHigh correlation
PM_RETIRO has 158 (50.3%) missing values Missing
PM_VALLECAS has 165 (52.5%) missing values Missing
PM_CIUDADLINEAL has 158 (50.3%) missing values Missing
TEMPERATURE has unique values Unique
WIND_SPEED has unique values Unique
formatted_date has unique values Unique
COMMULATIVE_PRECIPITATION has 130 (41.4%) zeros Zeros

Reproduction

Analysis started2021-05-04 16:39:45.532116
Analysis finished2021-05-04 16:42:47.818598
Duration3 minutes and 2.29 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

YEAR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:47.921329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12011
median2012.5
Q32014
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.716765664
Coefficient of variation (CV)0.0008530512616
Kurtosis-1.276741608
Mean2012.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum631925
Variance2.947284345
MonotocityIncreasing
2021-05-04T11:42:48.103842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201553
16.9%
201053
16.9%
201452
16.6%
201352
16.6%
201252
16.6%
201152
16.6%
ValueCountFrequency (%)
201053
16.9%
201152
16.6%
201252
16.6%
201352
16.6%
201452
16.6%
ValueCountFrequency (%)
201553
16.9%
201452
16.6%
201352
16.6%
201252
16.6%
201152
16.6%

Week
Real number (ℝ≥0)

Distinct53
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.66878981
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:48.304814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q340
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.13238219
Coefficient of variation (CV)0.5674191556
Kurtosis-1.19964865
Mean26.66878981
Median Absolute Deviation (MAD)13
Skewness0.0008578062142
Sum8374
Variance228.9889909
MonotocityNot monotonic
2021-05-04T11:42:48.548183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
276
 
1.9%
266
 
1.9%
246
 
1.9%
236
 
1.9%
226
 
1.9%
216
 
1.9%
206
 
1.9%
196
 
1.9%
186
 
1.9%
176
 
1.9%
Other values (43)254
80.9%
ValueCountFrequency (%)
16
1.9%
26
1.9%
36
1.9%
46
1.9%
56
1.9%
ValueCountFrequency (%)
532
 
0.6%
526
1.9%
516
1.9%
506
1.9%
496
1.9%

SEASON
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
84 
2
79 
3
78 
4
73 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4
ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%
2021-05-04T11:42:48.986016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-04T11:42:49.109683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring characters

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number314
100.0%

Most frequent character per category

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common314
100.0%

Most frequent character per script

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII314
100.0%

Most frequent character per block

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

PM_RETIRO
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct156
Distinct (%)100.0%
Missing158
Missing (%)50.3%
Infinite0
Infinite (%)0.0%
Mean89.50134276
Minimum18.66071429
Maximum266.6325301
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:49.296184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18.66071429
5-th percentile35.69544861
Q160.41791239
median80.12340648
Q3106.8875578
95-th percentile168.1834023
Maximum266.6325301
Range247.9718158
Interquartile range (IQR)46.46964539

Descriptive statistics

Standard deviation42.12679801
Coefficient of variation (CV)0.4706834189
Kurtosis1.451146598
Mean89.50134276
Median Absolute Deviation (MAD)23.35138157
Skewness1.057313675
Sum13962.20947
Variance1774.66711
MonotocityNot monotonic
2021-05-04T11:42:49.534063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.323353291
 
0.3%
38.744047621
 
0.3%
48.187878791
 
0.3%
101.53012051
 
0.3%
91.624242421
 
0.3%
139.90526321
 
0.3%
99.160714291
 
0.3%
99.212121211
 
0.3%
24.187878791
 
0.3%
140.56441721
 
0.3%
Other values (146)146
46.5%
(Missing)158
50.3%
ValueCountFrequency (%)
18.660714291
0.3%
22.952380951
0.3%
24.187878791
0.3%
26.048192771
0.3%
29.63751
0.3%
ValueCountFrequency (%)
266.63253011
0.3%
214.43902441
0.3%
194.40993791
0.3%
176.21604941
0.3%
175.68452381
0.3%

PM_VALLECAS
Real number (ℝ≥0)

MISSING

Distinct149
Distinct (%)100.0%
Missing165
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean91.08402763
Minimum6
Maximum248.3115942
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:49.774769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile32.46860894
Q161.50595238
median82.14102564
Q3107.9814815
95-th percentile181.0188406
Maximum248.3115942
Range242.3115942
Interquartile range (IQR)46.4755291

Descriptive statistics

Standard deviation45.79859942
Coefficient of variation (CV)0.5028170208
Kurtosis0.6674908442
Mean91.08402763
Median Absolute Deviation (MAD)21.56966618
Skewness0.9266844979
Sum13571.52012
Variance2097.511709
MonotocityNot monotonic
2021-05-04T11:42:49.995689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194.40119761
 
0.3%
53.39024391
 
0.3%
130.36690651
 
0.3%
10.454545451
 
0.3%
248.31159421
 
0.3%
75.21
 
0.3%
106.81
 
0.3%
46.819277111
 
0.3%
108.45783131
 
0.3%
107.98148151
 
0.3%
Other values (139)139
44.3%
(Missing)165
52.5%
ValueCountFrequency (%)
61
0.3%
9.3333333331
0.3%
10.454545451
0.3%
13.021
0.3%
19.140350881
0.3%
ValueCountFrequency (%)
248.31159421
0.3%
2081
0.3%
197.65060241
0.3%
196.5950921
0.3%
195.04729731
0.3%

PM_CIUDADLINEAL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct156
Distinct (%)100.0%
Missing158
Missing (%)50.3%
Infinite0
Infinite (%)0.0%
Mean89.10691932
Minimum13.7797619
Maximum249.6319018
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:50.210119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum13.7797619
5-th percentile34.19441587
Q159.19995301
median77.20045181
Q3108.1034979
95-th percentile168.0887157
Maximum249.6319018
Range235.8521399
Interquartile range (IQR)48.90354491

Descriptive statistics

Standard deviation43.45077231
Coefficient of variation (CV)0.4876251209
Kurtosis0.6841967409
Mean89.10691932
Median Absolute Deviation (MAD)22.34314661
Skewness0.9854582157
Sum13900.67941
Variance1887.969614
MonotocityNot monotonic
2021-05-04T11:42:50.459451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.764705881
 
0.3%
78.251497011
 
0.3%
45.542682931
 
0.3%
73.542168671
 
0.3%
55.777108431
 
0.3%
159.39156631
 
0.3%
174.80733941
 
0.3%
104.66071431
 
0.3%
90.237804881
 
0.3%
79.721
 
0.3%
Other values (146)146
46.5%
(Missing)158
50.3%
ValueCountFrequency (%)
13.77976191
0.3%
17.722891571
0.3%
23.207142861
0.3%
28.546583851
0.3%
30.234939761
0.3%
ValueCountFrequency (%)
249.63190181
0.3%
208.15151521
0.3%
194.16770191
0.3%
186.87730061
0.3%
183.21084341
0.3%

PM_CENTRO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct311
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.21641491
Minimum17.73214286
Maximum271.8433735
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:50.684850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17.73214286
5-th percentile36.55089286
Q166.08725163
median86.35825119
Q3118.6997166
95-th percentile182.0143933
Maximum271.8433735
Range254.1112306
Interquartile range (IQR)52.61246501

Descriptive statistics

Standard deviation45.41258061
Coefficient of variation (CV)0.4719837115
Kurtosis1.644776369
Mean96.21641491
Median Absolute Deviation (MAD)25.02874242
Skewness1.154750541
Sum30211.95428
Variance2062.302478
MonotocityNot monotonic
2021-05-04T11:42:50.966097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.458333332
 
0.6%
83.255952382
 
0.6%
70.833333332
 
0.6%
115.90277781
 
0.3%
105.86144581
 
0.3%
126.0059881
 
0.3%
80.017964071
 
0.3%
61.814814811
 
0.3%
70.269461081
 
0.3%
68.208333331
 
0.3%
Other values (301)301
95.9%
ValueCountFrequency (%)
17.732142861
0.3%
21.511904761
0.3%
23.916666671
0.3%
25.782894741
0.3%
29.185628741
0.3%
ValueCountFrequency (%)
271.84337351
0.3%
2661
0.3%
254.40476191
0.3%
242.93452381
0.3%
234.3273811
0.3%

DEW_POINT
Real number (ℝ)

HIGH CORRELATION

Distinct311
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.029029838
Minimum-21.68229167
Maximum23.61904762
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:51.501213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-21.68229167
5-th percentile-18.67529762
Q1-10.11309524
median2.74702381
Q314.74553571
95-th percentile21.37113095
Maximum23.61904762
Range45.30133929
Interquartile range (IQR)24.85863095

Descriptive statistics

Standard deviation13.383303
Coefficient of variation (CV)6.595912364
Kurtosis-1.344081318
Mean2.029029838
Median Absolute Deviation (MAD)12.31845238
Skewness-0.04594958744
Sum637.1153693
Variance179.1127991
MonotocityNot monotonic
2021-05-04T11:42:51.761034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18.083333332
 
0.6%
-1.3752
 
0.6%
17.47023812
 
0.6%
-1.9821428571
 
0.3%
7.3035714291
 
0.3%
-19.541666671
 
0.3%
-6.5238095241
 
0.3%
-19.458333331
 
0.3%
-9.2023809521
 
0.3%
19.946428571
 
0.3%
Other values (301)301
95.9%
ValueCountFrequency (%)
-21.682291671
0.3%
-21.184523811
0.3%
-20.898809521
0.3%
-20.821428571
0.3%
-20.160714291
0.3%
ValueCountFrequency (%)
23.619047621
0.3%
23.476190481
0.3%
23.3751
0.3%
23.244047621
0.3%
23.130952381
0.3%

HUMIDITY
Real number (ℝ≥0)

Distinct310
Distinct (%)99.0%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean54.72794453
Minimum18.3452381
Maximum97.22916667
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:51.991418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18.3452381
5-th percentile29.53690476
Q142.49404762
median55.4047619
Q365.9047619
95-th percentile79.2047619
Maximum97.22916667
Range78.88392857
Interquartile range (IQR)23.41071429

Descriptive statistics

Standard deviation15.6393306
Coefficient of variation (CV)0.2857649914
Kurtosis-0.7701986643
Mean54.72794453
Median Absolute Deviation (MAD)12.00595238
Skewness-0.07419284519
Sum17129.84664
Variance244.5886615
MonotocityNot monotonic
2021-05-04T11:42:52.234765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.40476192
 
0.6%
50.988095242
 
0.6%
65.886904762
 
0.6%
68.160714291
 
0.3%
30.7656251
 
0.3%
77.267857141
 
0.3%
33.613095241
 
0.3%
64.517857141
 
0.3%
85.511904761
 
0.3%
69.696428571
 
0.3%
Other values (300)300
95.5%
ValueCountFrequency (%)
18.34523811
0.3%
21.440476191
0.3%
21.505952381
0.3%
22.005952381
0.3%
24.130952381
0.3%
ValueCountFrequency (%)
97.229166671
0.3%
85.511904761
0.3%
84.880952381
0.3%
83.434523811
0.3%
82.797619051
0.3%

PREASSURE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct305
Distinct (%)97.4%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1016.527149
Minimum997.2678571
Maximum1036.470238
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:52.521998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum997.2678571
5-th percentile1002.133333
Q11008.095238
median1016.994048
Q31023.982143
95-th percentile1030.82619
Maximum1036.470238
Range39.20238095
Interquartile range (IQR)15.88690476

Descriptive statistics

Standard deviation9.321727306
Coefficient of variation (CV)0.009170170532
Kurtosis-1.137794415
Mean1016.527149
Median Absolute Deviation (MAD)7.916666667
Skewness-0.02445234755
Sum318172.9976
Variance86.89459996
MonotocityNot monotonic
2021-05-04T11:42:52.778044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.9642862
 
0.6%
1023.9821432
 
0.6%
1018.4583332
 
0.6%
1022.5595242
 
0.6%
1029.6252
 
0.6%
1004.8752
 
0.6%
1018.752
 
0.6%
1011.8571432
 
0.6%
1002.2023811
 
0.3%
1003.7976191
 
0.3%
Other values (295)295
93.9%
ValueCountFrequency (%)
997.26785711
0.3%
1000.4166671
0.3%
1000.5476191
0.3%
1000.6845241
0.3%
1000.8690481
0.3%
ValueCountFrequency (%)
1036.4702381
0.3%
1034.0476191
0.3%
1033.6354171
0.3%
1033.3541671
0.3%
1032.9464291
0.3%

TEMPERATURE
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.84754088
Minimum-1.862217018
Maximum31.47111632
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:53.036552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.862217018
5-th percentile3.592414374
Q18.428815379
median19.38095238
Q326.88729508
95-th percentile30.03449454
Maximum31.47111632
Range33.33333333
Interquartile range (IQR)18.4584797

Descriptive statistics

Standard deviation9.282351052
Coefficient of variation (CV)0.5200913177
Kurtosis-1.389319215
Mean17.84754088
Median Absolute Deviation (MAD)8.62363388
Skewness-0.20254832
Sum5604.127835
Variance86.16204105
MonotocityNot monotonic
2021-05-04T11:42:53.295600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.707259951
 
0.3%
6.3930523031
 
0.3%
27.64597971
 
0.3%
24.51854021
 
0.3%
26.714090551
 
0.3%
5.0659640911
 
0.3%
8.6178766591
 
0.3%
7.4859484781
 
0.3%
29.109679941
 
0.3%
24.333138171
 
0.3%
Other values (304)304
96.8%
ValueCountFrequency (%)
-1.8622170181
0.3%
0.15768930521
0.3%
1.2042349731
0.3%
1.9954462661
0.3%
2.3239656521
0.3%
ValueCountFrequency (%)
31.471116321
0.3%
31.114949261
0.3%
30.958821231
0.3%
30.934426231
0.3%
30.833807791
0.3%

WIND_SPEED
Real number (ℝ≥0)

UNIQUE

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.34791701
Minimum3.302202381
Maximum177.1026667
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:53.602778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.302202381
5-th percentile5.621282738
Q19.954895833
median15.47050595
Q327.25004464
95-th percentile65.0183404
Maximum177.1026667
Range173.8004643
Interquartile range (IQR)17.29514881

Descriptive statistics

Standard deviation23.12628606
Coefficient of variation (CV)0.9905074637
Kurtosis11.44095404
Mean23.34791701
Median Absolute Deviation (MAD)6.978541667
Skewness2.936798241
Sum7331.24594
Variance534.8251067
MonotocityNot monotonic
2021-05-04T11:42:53.914943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.260416671
 
0.3%
12.311964291
 
0.3%
22.38251
 
0.3%
20.628154761
 
0.3%
12.241904761
 
0.3%
10.736190481
 
0.3%
66.626607141
 
0.3%
10.462023811
 
0.3%
4.9999404761
 
0.3%
9.9175595241
 
0.3%
Other values (304)304
96.8%
ValueCountFrequency (%)
3.3022023811
0.3%
3.7068263471
0.3%
4.2002380951
0.3%
4.2195833331
0.3%
4.2651190481
0.3%
ValueCountFrequency (%)
177.10266671
0.3%
140.52690481
0.3%
138.55857141
0.3%
115.41803571
0.3%
110.73119051
0.3%

COMMULATIVE_PRECIPITATION
Real number (ℝ≥0)

ZEROS

Distinct127
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3192.679299
Minimum0
Maximum999990
Zeros130
Zeros (%)41.4%
Memory size4.9 KiB
2021-05-04T11:42:54.334389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.8
Q310.05
95-th percentile39.05
Maximum999990
Range999990
Interquartile range (IQR)10.05

Descriptive statistics

Standard deviation56432.25065
Coefficient of variation (CV)17.67551494
Kurtosis313.9999333
Mean3192.679299
Median Absolute Deviation (MAD)0.8
Skewness17.72004233
Sum1002501.3
Variance3184598913
MonotocityNot monotonic
2021-05-04T11:42:54.729657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0130
41.4%
0.110
 
3.2%
0.75
 
1.6%
0.25
 
1.6%
4.94
 
1.3%
3.14
 
1.3%
0.84
 
1.3%
3.44
 
1.3%
10.13
 
1.0%
1.13
 
1.0%
Other values (117)142
45.2%
ValueCountFrequency (%)
0130
41.4%
0.110
 
3.2%
0.25
 
1.6%
0.31
 
0.3%
0.42
 
0.6%
ValueCountFrequency (%)
9999901
0.3%
2231
0.3%
102.31
0.3%
75.81
0.3%
74.11
0.3%

formatted_date
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012766.688
Minimum2010010
Maximum2015530
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:42:55.168351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2010010
5-th percentile2010166.5
Q12011262.5
median2012765
Q32014267.5
95-th percentile2015373.5
Maximum2015530
Range5520
Interquartile range (IQR)3005

Descriptive statistics

Standard deviation1723.421958
Coefficient of variation (CV)0.0008562452708
Kurtosis-1.256219614
Mean2012766.688
Median Absolute Deviation (MAD)1505
Skewness0.003308513388
Sum632008740
Variance2970183.244
MonotocityStrictly increasing
2021-05-04T11:42:55.490840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20101101
 
0.3%
20152501
 
0.3%
20112901
 
0.3%
20133401
 
0.3%
20102701
 
0.3%
20150601
 
0.3%
20123201
 
0.3%
20143701
 
0.3%
20113001
 
0.3%
20133501
 
0.3%
Other values (304)304
96.8%
ValueCountFrequency (%)
20100101
0.3%
20100201
0.3%
20100301
0.3%
20100401
0.3%
20100501
0.3%
ValueCountFrequency (%)
20155301
0.3%
20155201
0.3%
20155101
0.3%
20155001
0.3%
20154901
0.3%
Distinct313
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Minimum2010-01-10 00:00:00
Maximum2016-01-10 00:00:00
2021-05-04T11:42:55.780804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:56.074662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2021-05-04T11:40:55.059874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:40:57.434438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:40:58.052756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:40:58.588559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:40:59.277717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:40:59.758175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:41:00.074511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:41:00.361043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:12.978026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:13.300164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:13.561468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:13.820283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:14.075112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:14.307492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:14.500411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:14.697882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:14.939239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:15.143690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:15.375589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:15.622315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:15.931274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:16.135727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:16.381072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:16.575577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:16.771030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:17.014384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:17.195898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:17.375777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:17.546292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:17.707859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:17.876408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:18.067895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:18.351186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:18.626355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:18.849234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:19.043005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:19.297568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:19.513754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:19.692787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:19.873305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:20.175498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:20.544511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:20.722068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:20.950841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:21.152301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:21.345783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:21.540267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:21.744717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:22.010135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:22.269408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:22.517745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:22.754758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:22.974356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:23.211234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:23.382777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:23.577260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:23.791682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:23.975702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:24.163206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:24.368658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:24.556669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:24.763594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:24.978534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:25.148112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:25.351537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:25.520087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:25.691628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:25.901117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:26.105570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:26.301870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:26.491886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:26.684371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:26.879850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:27.082339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:27.262826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:27.432411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:27.611929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:27.772629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:27.960178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:28.184547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:28.388004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:28.575504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:28.919603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:29.124062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:29.314566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:29.540976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:29.749427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:29.954869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:30.167302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:30.407661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:30.645668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:30.854110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:31.085896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:31.325806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:31.551169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:31.769588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:32.005986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:32.254308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:32.467742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:32.672738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:32.873987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:33.077414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:33.277878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:33.482330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:33.714709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:33.929136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:34.161798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:34.389692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:34.617113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:34.810139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:35.077395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:35.262898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:35.463365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:35.674309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:35.857839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:36.056139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:36.272070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:36.495471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:36.704913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:36.911361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:37.182221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:37.403629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:37.634522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:37.882260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:38.168499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:38.424810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:38.683122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:38.963402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:39.189801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:39.417740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:39.842427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:40.090764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:40.354059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:40.556517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:40.792891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:40.978395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:41.171880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:41.365418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:41.566876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:41.756370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:41.984761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:42.199694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:42.406190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:42.641751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:42.845772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:43.053185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:43.254649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:43.448129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:43.650590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:43.835603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:44.031124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:44.234547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:44.463954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:44.687357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:44.887821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:42:45.108233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-04T11:42:56.401785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-04T11:42:56.938919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-04T11:42:57.416299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-04T11:42:57.880749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-04T11:42:45.582964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-04T11:42:46.539376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-04T11:42:47.134534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-04T11:42:47.554311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

YEARWeekSEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATIONformatted_dateYearWeek
0201014NaNNaNNaN74.119048-19.97619050.6785711031.470238-1.86221732.7785710.020100102010-01-10
1201024NaNNaNNaN88.250000-18.08333349.7500001031.3452380.15768943.4644640.020100202010-01-17
2201034NaNNaNNaN131.897059-14.20238146.4047621028.2559525.01717438.8438100.020100302010-01-24
3201044NaNNaNNaN63.413534-17.74404829.6845241023.7380957.00292751.7404170.020100402010-01-31
4201054NaNNaNNaN76.416667-14.01785752.9345241027.6250003.83645610.4060710.020100502010-02-07
5201064NaNNaNNaN80.017964-16.98214338.1309521029.4166675.14402814.1273210.020100602010-02-14
6201074NaNNaNNaN98.898810-16.13095236.3392861022.5595246.39305211.9607140.020100702010-02-21
7201084NaNNaNNaN133.523810-5.49404863.1726191015.5059528.61787717.3730364.720100802010-02-28
8201091NaNNaNNaN94.410714-10.15476252.6071431025.4642867.48594824.8736315.120100902010-03-07
92010101NaNNaNNaN70.553571-10.55952449.1011901024.3392868.14949326.57178610.120101002010-03-14

Last rows

YEARWeekSEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATIONformatted_dateYearWeek
304201544329.63750074.50000028.54658429.839286-3.04166750.1547621024.22023814.60929029.8995834.320154402015-11-08
305201545394.52381052.19354896.25595293.3511901.90476275.5041321025.19834712.83333317.471548999990.020154502015-11-15
3062015463175.684524208.000000183.210843175.9523814.898204NaNNaN13.0709733.7068260.020154602015-11-22
307201547357.36363670.64705959.29813760.4821430.57142997.2291671033.3541679.86202216.3892267.120154702015-11-29
3082015483140.5644176.000000148.794521135.416667-9.20238170.2500001031.9166673.98770520.5636310.020154802015-12-06
3092015493176.216049174.142857168.098765160.455090-7.70658759.7005991024.9520967.98115257.3051500.020154902015-12-13
3102015504170.718563193.800000179.491018169.714286-3.57738174.4107141027.9880958.4032015.0036310.020155002015-12-20
311201551490.79518113.02000097.02994097.454545-9.68452455.4047621029.7797627.08099177.8649400.720155102015-12-27
3122015524266.632530248.311594249.631902254.404762-6.52381076.7500001027.3452385.5392274.2002380.020155202016-01-03
3132015534139.905263181.055556150.473684154.229167-9.22916763.6875001031.6875005.47336112.6192710.020155302016-01-10